Abstract

The wealth of computerised medical information becoming readily available presents the opportunity to examine

patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions.

In this paper sequential rule mining is applied to a General

Practice database to find rules involving a patients age, gender and medical history. By incorporating these rules into current health-care a patient can be highlighted as susceptible to a future illness based on past or current illnesses, gender and year of birth. This knowledge has the ability to greatly improve health-care and reduce health-care costs.

Item Type:

Conference or Workshop Item
(Paper)

Additional Information:

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Schools/Departments:

University of Nottingham, UK > Faculty of Science > School of Computer Science